Cumulative systolic blood pressure load, which can be calculated from serial blood pressure measurements, may provide better prediction of major cardiovascular events, compared with traditional blood pressure measures, a new study suggests.
“Our results suggest that cumulative blood pressure load is an independent predictor of cardiovascular events and should be used in future cardiovascular risk prediction algorithms,” the authors, led by Nelson Wang, MD, George Institute for Global Health, Sydney, conclude.
The study was published online in the Journal of the American College of Cardiology.
The researchers explain that the management of hypertension has traditionally centered around blood pressure measurements taken at a single timepoint, with adequate control defined as those measurements being below a predefined target threshold.
However, this approach fails to recognize blood pressure as a continuous measure that fluctuates over time and does not acknowledge that the most recently recorded measurement may not reflect previous blood pressure control.
More recently, studies have defined the time a patient spends below blood pressure target, or TIme at TaRgEt (TITRE), as a novel marker of cardiovascular risk that is independent of mean blood pressure.
Although TITRE has the added advantage of incorporating duration of control, it is unable to characterize the magnitude of blood pressure elevation, the researchers note.
They point out that an optimal measure as a risk factor for cardiovascular disease would account for both the magnitude and duration of elevated blood pressure.
Such a measure is cumulative blood pressure load, defined as the area under the curve (AUC) expressed in units of mm Hg by time.
The only prior study of this measure was small and retrospective and calculated cumulative blood pressure load from ambulatory blood pressure monitoring estimated over a short (24-hour) period.
Therefore, the aim of the current study was to estimate the association between cumulative systolic blood pressure load over a longer period (24 months) and subsequent major cardiovascular events.
To do this, the researchers conducted a post-hoc analysis of 9,338 patients with type 2 diabetes in the ADVANCE-ON study.
Cumulative systolic blood pressure load was defined as the AUC for systolic blood pressure values above 130 mm Hg divided by the AUC for all measured systolic blood pressure values over a 24-month exposure period.
Over a median 7.6 years of follow-up, 1,469 major cardiovascular events, 1,615 deaths, and 660 cardiovascular deaths occurred.
Results showed that each one standard deviation increase in cumulative systolic blood pressure load was associated with a 14% increase in major cardiovascular events, a 13% increase in all-cause mortality, and a 21% increase in cardiovascular death.
Cumulative systolic blood pressure load outperformed mean systolic blood pressure, time-below-target, and visit-to-visit systolic blood pressure variability for the prediction of cardiovascular events and death and also discriminated risk and reclassified more patients’ risk correctly than the other measures.
“Small improvements in risk prediction can have a major impact when scaled up across large high-risk populations. Furthermore, cumulative systolic pressure load may also prove useful to inform the design of future clinical trials,” the researchers say.
Although the present study only assessed cumulative systolic blood pressure load over 24 months, clinicians should recognize the importance of this measure over a lifetime, they note.
“This approach emphasizes the importance of early blood pressure–lowering interventions to reduce the cumulative systolic blood pressure load that each individual experiences over their lifetime,” they conclude.
The researchers suggest that, based on these results, cumulative systolic blood pressure load and visit-to-visit systolic blood pressure variability “should be used in conjunction in future cardiovascular risk prediction algorithms.”
In an accompanying editorial, Donald Lloyd-Jones, MD, Northwestern Feinberg School of Medicine, Chicago, says that before routinely adopting these new measures, several additional questions need to be addressed.
He notes that many patients in the current study already had cardiovascular disease, and it is not known whether the benefit was consistent among those with and without cardiovascular disease. In addition, longer term data using blood pressure measurements in the real-world clinical setting would be desirable, as well as information on whether these new measures add incremental value to existing risk prediction equations.
“Certainly, the next guidelines should reconsider all types of blood pressure measures, and other potential predictors, to optimize risk estimation and identification of patients with greatest net benefit from risk-reducing therapies,” Dr. Lloyd-Jones comments.
“Ultimately, clinicians should leverage as much information on their patients as possible to understand their blood pressure–related cardiovascular risk, to identify those who may be more likely have occult or emerging subclinical target organ damage, and to identify those who may have particular net benefit from earlier or more intensive treatment,” he concludes.
“These opportunities are more readily available with integration of data that allow for visualization of longer-term blood pressure patterns and incorporation of home monitoring and ambulatory monitoring data to monitor out-of-office blood pressure levels and control.”
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